Event clustering of images using foreground/background segmentation

ABSTRACT

An event clustering method uses foreground and background segmentation for clustering images from a group into similar events. Initially, each image is divided into a plurality of blocks, thereby providing block-based images. Utilizing a block-by-block comparison, each block-based image is segmented into a plurality of regions comprising at least a foreground and a background. One or more luminosity, color, position or size features are extracted from the regions and the extracted features are utilized to estimate and compare the similarity of the regions comprising the foreground and background in successive images in the group. Then, a measure of the total similarity between successive images is computed, thereby providing image distance between successive images, and event clusters are delimited from the image distances.

FIELD OF THE INVENTION

[0001] The invention relates generally to the field of auto albuming ofconsumer-captured images, and in particular to a system for classifyingconsumer-captured images by event similarity.

BACKGROUND OF THE INVENTION

[0002] Pictorial images are often classified by the particular event,subject or the like for convenience of retrieving, reviewing andalbuming of the images. Typically, this has been achieved by manuallysegmenting the images, or by an automated method that groups the imagesby color, shape or texture in order to partition the images into groupsof similar visual content. It is clear that an accurate determination ofcontent would make the job easier. Although not directed to eventclassification, there is a body of prior art addressing content-basedimage retrieval and the content description of images. Some typicalreferences are described below.

[0003] In U.S. Pat. No. 6,072,904, “Fast image retrieval usingmulti-scale edge representation of images”, a technique for imageretrieval uses multi-scale edge characteristics. The target image andeach image in the data base are characterized by a vector of edgecharacteristics within each image. Retrieval is effected by a comparisonof the characteristic vectors, rather than a comparison of the imagesthemselves. In U.S. Pat. No.5,911,139, “Visual image database searchengine which allows for different schema”, a visual informationretrieval engine is described for content-based search and retrieval ofvisual objects. It uses a set of universal primitives to operate on thevisual objects, and carries out a heterogeneous comparison to generate asimilarity score. U.S. Pat. No. 5,852,823, “Image classification andretrieval system using a query-by-example paradigm”, teaches a paradigmfor image classification and retrieval by query-by-example. The methodgenerates a semantically based, linguistically searchable, numericdescriptor of a pre-defined group of input images and which isparticularly useful in a system for automatically classifying individualimages.

[0004] The task addressed by the foregoing three patents is one of imageretrieval, that is, finding similar images from a database, which isdifferent from the task of event clustering for consumer images, such asphoto album organization for consumer images. The descriptors describedin these patents do not suggest using foreground and backgroundsegmentation for event clustering. Most importantly, the segmentation ofimages into foreground and background is not taken into account as animage similarity measure.

[0005] Commonly-assigned U.S. Pat. No. 6,011,595, “Method for segmentinga digital image into a foreground region and a key color region”, whichissued Jan. 4, 2000 to T. Henderson, K. Spaulding and D. Couwenhoven,teaches image segmentation of a foreground region and a key colorbackdrop region. The method is used in a “special effects” process forcombining a foreground image and a background image. However, theforeground/background separation is not used for image similaritycomparison.

[0006] Commonly assigned U.S. patent application Ser. No. 09/163,618, “Amethod for automatically classifying images into events”, filed Sep. 30,1998 in the names of A. Loui and E. Pavie, and commonly-assigned U.S.patent application Ser. No. 09/197,363, “A method for automaticallycomparing content of images for classification into events”, filed Nov.20, 1998 in the names of A. Loui and E. Pavie, represent a continuouseffort to build a better system of event clustering for consumer images,albeit with different technical approaches. Ser. No. 09/163,618discloses event clustering using date and time information. Ser. No.09/197,363 discloses a block-based histogram correlation method forimage event clustering, which can be used when date and time informationis unavailable. It teaches the use of a main subject area (implementedby fixed rectangle segmentation) for comparison, but does not proposeany automatic method of performing foreground/background segmentation,which would be more accurate than a fixed rectangle.

[0007] Two articles—one by A. Loui, and A. Savakis, “Automatic imageevent segmentation and quality screening for albuming applications,”Proceedings IEEE ICME 2000, New York, August, 2000 and the other by JohnPlatt, “AutoAlbum: Clustering digital photographs using probabilisticmodel merging”, Proceedings IEEE Workshop on Content-based Access ofImage and Video Libraries, 2000 —specifically relate to event clusteringof consumer images; however they do not look into regions of images andtake advantage of the foreground and background separation. Loui andSavakis teach an event clustering scheme based on date and timeinformation and general image content. Platt teaches a clustering schemebased on probabilistic merging of images. Both of them fail to addressthe foreground and background separation.

[0008] What is needed is a system for segmenting images into coarseregions such as foreground and background and deriving global similaritymeasures from the similarity between the foreground/background regions.Furthermore, such a system should not become confused by unnecessarydetails and irrelevant clusters in consumer images.

SUMMARY OF THE INVENTION

[0009] The present invention is directed to overcoming one or more ofthe problems set forth above. Briefly summarized, according to oneaspect of the present invention, an event clustering method usesforeground and background segmentation for clustering images from agroup into similar events. Initially, each image is divided into aplurality of blocks, thereby providing block-based images. Utilizing ablock-by-block comparison, each block-based image is segmented into aplurality of regions comprising at least a foreground and a background.One or more features, such as luminosity, color, position or size, areextracted from the regions and the extracted features are utilized toestimate and compare the similarity of the regions comprising theforeground and background in successive images in the group. Then, ameasure of the total similarity between successive images is computed,thereby providing image distance between successive images, and eventclusters are delimited from the image distances.

[0010] This invention further includes a system for event clustering ofconsumer images using foreground/background segmentation, which can beused for auto albuming and related image management and organizationtasks. The goal of the disclosed system is to classify multiple consumerphotograph rolls into several events based on the image contents, withemphasis on the separation of foreground and background. An importantaspect of this system is automatic event clustering based on foregroundand background segmentation, leading to better similarity matchingbetween images and performance improvement. Another advantage of thepresent invention is the use of a block-based approach for segmentation,which will be more computationally efficient than a pixel-basedsegmentation scheme.

[0011] These and other aspects, objects, features and advantages of thepresent invention will be more clearly understood and appreciated from areview of the following detailed description of the preferredembodiments and appended claims, and by reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012]FIG. 1 shows a block diagram of event clustering using block-basedforeground/background segmentation according to the invention.

[0013]FIGS. 2A and 2B show details of the block-based segmentationtechnique shown in FIG. 1, in particular showing the joining of blockboundary separations to form regions.

[0014]FIG. 3 demonstrates an example of foreground and backgroundsegmentation according to the invention.

[0015]FIG. 4 illustrates the comparison of distance (dissimilarity)measures generated for regions comprising the foreground and backgroundin two images.

[0016]FIGS. 5A, 5B and 5C show the use of memory to compute distancebetween successive and more distant images in a chronological sequenceof such images.

[0017]FIG. 6 shows an example of foreground and background separationfor four consumer images.

[0018]FIG. 7 shows a similarity comparison between the foreground andbackground regions of the four images shown in FIG. 6.

[0019]FIG. 8 is a precision recall plot showing the event clusteringperformance using foreground and background segmentation.

DETAILED DESCRIPTION OF THE INVENTION

[0020] In the following description, a preferred embodiment of thepresent invention will be described in terms that would ordinarily beimplemented as a software program. Those skilled in the art will readilyrecognize that the equivalent of such software may also be constructedin hardware. Because image manipulation algorithms and systems are wellknown, the present description will be directed in particular toalgorithms and systems forming part of, or cooperating more directlywith, the system and method in accordance with the present invention.Other aspects of such algorithms and systems, and hardware and/orsoftware for producing and otherwise processing the image signalsinvolved therewith, not specifically shown or described herein, may beselected from such systems, algorithms, components and elements known inthe art. Given the system as described according to the invention in thefollowing materials, software not specifically shown or described hereinthat is useful for implementation of the invention is conventional andwithin the ordinary skill in such arts.

[0021] Still further, as used herein, the computer program may be storedin a computer readable storage medium, which may comprise, for example;magnetic storage media such as a magnetic disk (such as a hard drive ora floppy disk) or magnetic tape; optical storage media such as anoptical disc, optical tape, or machine readable bar code; solid stateelectronic storage devices such as random access memory (RAM), or readonly memory (ROM); or any other physical device or medium employed tostore a computer program.

[0022] This invention discloses a system for event clustering ofconsumer images using foreground/background segmentation, which can beused for auto albuming and related image management and organizationtasks. It is a challenging task to automatically organize consumerimages without any content description into semantically meaningfulevents. The goal of the disclosed system is to classify multipleconsumer photograph rolls into several events based on the imagecontents, with emphasis on the separation of foreground and background.An important aspect of this disclosure is automatic event clusteringbased on foreground and background segmentation, leading to bettersimilarity matching between images and performance improvement.

[0023] Referring first to FIG. 1, an event clustering system accordingto the invention operates on a group of images 8, which may be imagesscanned from a roll of film or provided from other sources, such as froma database of images. The images are typically consumer images sincethat is where the greater value for event clustering may be found, butthere is no requirement for the images to be such. The event clusteringalgorithm is composed of four major modules, as follows:

[0024] A first module 10 for segmenting each of the images in the groupinto regions comprising a foreground and a background;

[0025] A second module 12 for extracting one or more low-level features,such as luminosity, color, position and size, from the regionscomprising the foreground and the background;

[0026] A third module 14 for computing distances (dissimilarities)between successive images considering all the regions in the foregroundand the background, meanwhile taking advantage of the memory of frameorder; and

[0027] A fourth module 16 for determining the greatest distance betweenimages in the group, including successive images and more distantlyseparated images, in order to delimit the clusters.

[0028] Since the invention may also be thought of as a method for eventclustering, each of the foregoing modules may also be thought of as thesteps that would be implemented in performing the method.

[0029] Since a fine and accurate segmentation of background andforeground is difficult and computationally expensive, a coarsesegmentation of foreground and background is preferred and adequatelyserves the purpose. Accordingly, in the first module 10, the image isdivided into blocks and the dissimilarity between neighboring blocks iscomputed to connect different block-to-block separations to formregions, as shown in FIGS. 2A and 2B. More specifically, an image isfirst divided into rectangular blocks with respect to a grid outline 20.Then, for each rectangular block 22, its distance (dissimilarity) iscomputed with respect to its neighboring blocks using the features thatwill be described subsequently in connection with the second module 12.(Preferably, the distances calculated in equations (3) and (4) are usedto establish block-to-block dissimilarity.) The greatest distances arethen identified and used to establish initial separation boundariesbetween the rectangular blocks.

[0030] Where the initial separation boundaries are isolated from eachother or the image border, they are then connected to each other or theimage border along intervening block boundaries of greatest remainingdistance (as shown by the arrow connections 26 in FIG. 2A) until allseparation boundaries are connected to form a plurality of regions 28 a,28 b . . . 28 e. Then the regions are merged two by two by computing thedistances (dissimilarity) between all the regions 28 a . . . 28 eandmerging those regions that have the smallest distances. This is repeateduntil two combinations of regions remain. Different regioncharacteristics, such as size, position and contact with the imageborders, are then used to distinguish background from foreground. Forinstance, a large centrally positioned combination of regions is likelyto be a foreground and the remaining combination of outwardly positionedregions is likely to be a background. As shown in FIG. 2B, thisoptimally results in two distinct combinations of regions: regions 28 aand 28 e comprising a background 30 and regions 28 b, 28 c and 28 dcomprising a foreground 32. As an example of an actual image, FIG. 3shows the approximate foreground and background segmentation of alighthouse image using the foregoing block-based approach.

[0031] In certain situations, especially where a small region of theimage is quite different from the rest of the image, the block-basedsegmentation process may provide a foreground or a background of only afew blocks. These few blocks may not be sufficient for an accuratebackground/foreground segmentation. To avoid this outcome, when apredetermined number of regions formed in the segmentation process areeach less than a predetermined size, the foreground is approximated by arectangle of fixed size and position (the predetermined numbers may beempirically determined.) Intuitively, this rectangle position is in thecenter between left and right borders and just below the center betweentop and bottom borders. As will be shown later in connection with FIG.8, allowing for this variation from the main segmentation process forthese certain situations provides improved results.

[0032] While this block-based segmentation is preferred for itssimplicity and efficiency, other automated segmentation techniques maybe employed. For example, the segmentation method employed in commonlyassigned, copending U.S. patent application Ser. No. 09/223,860,entitled “Method for Automatic Determination of Main Subjects inPhotographic Images” filed Dec. 31, 1998 in the names of J. Luo et al.,which is incorporated herein by reference, may be used, albeit at acertain price in computational complexity. This segmentation methodprovides a two-level segmentation, as follows

[0033] A first level composed of several regions, which are homogeneous.

[0034] A second level that groups the regions from the first level toform a foreground, a background and an intermediate region.

[0035] In addition, in certain situations the block-based segmentationprocess may turn up an uncertain region that will best be categorized asan intermediate region since its distance from other regions is notsufficient to clearly associate it with either background or foreground.

[0036] After the image has been segmented in the first module 10, one ormore low level features such as luminosity, color, position, and sizeare extracted in the second module 12 from the regions comprising theforeground 30 and the background 32. At this stage, each featureextraction algorithm also has at its disposal the original imageinformation and the mask(s) created as a result of the segmentation,which are used to separate the foreground and background imageinformation. The feature extraction algorithm for luminosity is based onthe formula for YUV conversion:

y=0.299×R+0. 587×G+0.114×B  Eq. (1)

[0037] where Y is luminance and RGB represents the color informationobtained from individual pixels of the image. The mean luminosity iscomputed for the regions comprising the foreground and background. Thedistance between two different regions is simply the absolute value ofthe difference of these means. Based on this feature, images may beseparated into outdoors images, well highlighted images, and imagestaken during the night, indoor, or in a dark environment.

[0038] To compute the color feature of a region, the hue (H), intensity(I) and saturation (S) are first quantized using the equations:$\begin{matrix}\left\{ \begin{matrix}{I = \frac{R + G + B}{3}} \\{S = {1 - \frac{\min \left( {R,G,B} \right)}{I}}} \\{H = {\cos^{- 1}\left( \frac{{\frac{1}{2} \times \left( {R - G} \right)} + \left( {R - B} \right)}{\left( \left( {\left( {R - G} \right)^{2} + {\left( {R - B} \right)\left( {G - B} \right)}} \right)^{1/2} \right)} \right.}}\end{matrix} \right. & {{Eq}.\quad (2)}\end{matrix}$

[0039] Every region in the image is represented by a color set. Tocompute the distance between two color sets c₀ and c₁, the distance iscalculated and then a component is added to account for the differentsizes of the regions, thereby giving more or less emphasis to eachcomponent. Given two color set components m₀=(h₀,i₀,s₀) andm₁=(h₁,i₁,s₁), the distance is calculated as follows:

d _(m0,m1) =h _(coeff)×min(|h ₁ −h ₀ |, h _(max) −|h ₁ −h ₀|)+i _(coeff)×|i ₁ −i ₀ |+s _(coeff) ×|s ₁ −s ₀|  Eq. (3)

[0040] where h_(coeff), i_(coeff) and s_(coeff) are determined by theuser.

[0041] Then distance between the two color sets c₀ and c₁ is determinedby $\begin{matrix}{d_{{c0},{c1}} = {\frac{1}{n_{0} \times n_{1}}{\sum\limits_{m_{0} \in \quad c_{0}}{\sum\limits_{m_{1} \in \quad c_{1}}{{c_{0}\left\lbrack m_{0} \right\rbrack} \cdot d_{m_{0},m_{1}} \cdot {c_{1}\left\lbrack m_{1} \right\rbrack}}}}}} & {{Eq}.\quad (4)}\end{matrix}$

[0042] where n₀ and n₁ are the number of pixels of regions 0 and 1 andc[m] is the number of pixel in color set c for level m.

[0043] It may be further desirable to consider the position and sizefeatures of the different regions. For example, higher weights may beassigned to the regions in the central part of the image.

[0044] After the low level features and distances have been extractedand the regions comprising the foreground and background have beendetermined for each image, distances are computed in the module 14between different regions (resulting from the segmentation) of differentimages 40 and 42 from the same group, as shown in FIG. 4. The goal ofthis step is to compute the distances between different images,considering all the regions in each image, where the distance metricsare those used for the block-based segmentation (e.g., the luminositydistance and/or the color set distance).

[0045] For each image, there are different regions comprising theforeground and background and perhaps further regions comprising anintermediate area. The goal is to compare regions of the same type,e.g., foreground to foreground and background to background, except inthe case of the intermediate areas, where they are compared with eachother and with regions comprising both background and foreground. Morespecifically, referring to FIG. 4, three regions 44 a, 44 b, 44 ccomprising the foreground of image 40 are compared to two regions 46 aand 46 b comprising the foreground of image 42. Likewise, although notseparately enumerated, the three regions (indicated by check marks)comprising the background of image 40 are compared to the single region(also indicated by a check mark) comprising the background of image 42.FIG. 4 also illustrates the situation of intermediate areas, where thetwo regions comprising the intermediate areas of images 40 and 42 arecompared with each other and with the regions comprising the foregroundand background of the two images.

[0046] After the distances between the different regions comprising theforeground and background in successive images have been computed, atotal distance between the images is computed in module 14 using aharmonic mean equation, as follows: $\begin{matrix}{{{harmonic}\quad {mean}\quad \left( {a_{1},a_{2},\ldots \quad,a_{n}} \right)} = \frac{1}{\frac{1}{a_{0}} + \frac{1}{a_{1}} + \ldots + \frac{1}{a_{n}}}} & {{Eq}.\quad (5)}\end{matrix}$

[0047] where a_(i) is the dissimilarity (distance) between theindividual regions comprising the foreground and background in therespective images.

[0048] After the total dissimilarity between successive images has beendetermined in the module 14, event clusters are determined in module 16according to the image distance of the respective images. Given thedistances between successive images, a threshold may be chosen and alldistances above this threshold are determined to be separations betweendifferent event clusters. Conversely, differences below the thresholdare not to be taken as event separations, and such images belong to thesame event. The threshold may be a constant number or a function of thestatistical characteristics of the distance distribution (such as themaximum distance, the average distance, the variance, and so on), or thenumber of desired clusters, or the entropy of the whole distribution(entropy thresholding is described in N. R. Pal and S. K. Pal. “EntropicThresholding,” Signal Processing, 16, pp. 97-108, 1989). In a preferredimplementation, the threshold is a function of the average and themaximum distances in the group of images.

[0049] Sometimes, there may be a chronological order of several imagesapparently belonging to the same event, and all are similar except forone (or a few) images in between. To take advantage of the chronologicalorder of the images, memory can be employed not only to compute thedistance between successive (that is, adjacent) images, but also tocompute the distance between more distantly separated images. As shownin FIGS. 5A, 5B and 5C, when a decision is made on whether there is anevent break, the adjacent images 50 (no memory) may be compared (FIG.5A), every other image 52 (1-image memory) may be compared (FIG. 5B) orevery other two images 54 (2-image memory) may be compared (FIG. 5C).More specifically, the total distance measured by the harmonic mean maybe taken between the respective images to determine if the group ofimages belong to the apparent event.

[0050] It facilitates an understanding of the invention to examine anevent clustering example for several images using foreground andbackground separation. FIG. 6 shows an example of foreground andbackground separation for four typical consumer images. Two event breaksare detected, one event break 60 between images 2 and 3, and the otherevent break 62 between images 3 and 4. The first row of images shows thefour images. The second and third rows show the results of 1-level and2-level foreground and background segmentation. FIG. 6 also demonstratesthe foreground and background separation results using a block-basedapproach. The regions comprising the foreground and background betweenthese images are compared for similarity, as shown in FIG. 7, and theirrespective distances are used for event clustering.

[0051] A precision recall plot is used to evaluate the event-clusteringalgorithm. The recall and precision are defined as $\begin{matrix}{{{recall} = \frac{{\# \quad {correct}} + 1}{{\# \quad {correct}} + {\# \quad {missed}} + 1}}{{precision} = \frac{{\# \quad {correct}} + 1}{{\# \quad {correct}} + {\# \quad {false\_ positive}} + 1}}} & {{Eqs}.\quad (6)}\end{matrix}$

[0052] where recall indicates how many event breaks are missed andprecision shows how many event breaks are falsely detected while thereis no event break. The numbers are between 0 and 1. The bigger thenumbers, the better the system performance.

[0053] The event-clustering algorithm has been tested on 2600 typicalconsumer images. The recall/precision performance with no memory isshown in FIG. 8. The basic approach used the block-based foreground andbackground separation. The improved approach indicates a combination ofblock based foreground/background segmentation and, for the specialsituation described earlier, fixed rectangular foreground/backgroundseparation, where the segmentation is simply replaced by a fixedrectangle in the foreground. To this end, the system has achievedprecision of 58% and recall of 58% on event clustering of 2600 consumerimages using 2-image memory.

[0054] The subject matter of the present invention relates to digitalimage understanding technology, which is understood to mean technologythat digitally processes a digital image to recognize and thereby assignuseful meaning to human understandable objects, attributes or conditionsand then to utilize the results obtained in the further processing ofthe digital image.

[0055] The invention has been described in detail with particularreference to certain preferred embodiments thereof, but it will beunderstood that variations and modifications can be effected within thespirit and scope of the invention. For instance, the idea of usingforeground and background segmentation for event clustering can beextended to using multiple regions as well.

Parts List

[0056]10 first module

[0057]12 second module

[0058]14 third module

[0059]16 fourth module

[0060]20 grid outline

[0061]22 rectangular block

[0062]24 initial separation

[0063]26 arrow extensions

[0064]28 regions

[0065]30 background

[0066]32 foreground

[0067]40 image

[0068]42 image

[0069]44 a . . . regions comprising foreground

[0070]46 a . . . regions comprising foreground

[0071]50 adjacent image

[0072]52 every other image

[0073]54 every other two images

[0074]60 first event break

[0075]62 second event break

What is claimed is:
 1. An event clustering method using foreground andbackground segmentation for clustering images from a group into similarevents, said method including the steps of: (a) segmenting each imageinto a plurality of regions comprising at least a foreground and abackground; (b) extracting one or more features from the regionscomprising the foreground and background, said features including atleast one of luminosity, color, position and size of the regions; (c)utilizing the features to compute the similarity of the regionscomprising the foreground and background of successive images in thegroup; (d) computing a measure of the total similarity betweensuccessive images, thereby providing a measure of image distance betweensuccessive images; and (e) delimiting event clusters from the imagedistances, whereby the event clusters include groups of imagespertaining to the same events.
 2. The method as claimed in claim 1wherein the step (c) utilizes the features to generate a distancemeasure that indicates the similarity or dissimilarity between theregions.
 3. The method as claimed in claim 1 wherein if a predeterminednumber of regions formed in step (a) are each less than a predeterminedsize, then a fixed region is generated for the foreground.
 4. The methodas claimed in claim 1 wherein the group of images are arranged in achronological order and step (c) further utilizes the features toestimate and compare the similarity of regions comprising foreground andbackground in every other image in the group and step (d) computes ameasure of the total similarity between every other image, therebyproviding image distance between successive images and every otherimage.
 5. The method as claimed in claim 1 wherein the group of imagesare arranged in a chronological order and step (c) further utilizes thefeatures to estimate and compare the similarity of regions comprisingforeground and background in every other two images in the group andstep (d) computes a measure of the total similarity between every othertwo images, thereby providing image distance between successive imagesand every other two images.
 6. A computer storage medium havinginstructions stored therein for causing a computer for perform themethod of claim
 1. 7. An event clustering method using foreground andbackground segmentation for clustering images from a group into similarevents, said method including the steps of: (a) dividing each image intoa plurality of blocks, thereby providing block-based images; (b)utilizing a block-by-block comparison to segment each block- based imageinto a plurality of regions comprising at least a foreground and abackground; (c) extracting one or more features from the regionscomprising the foreground and background, said features including atleast one of luminosity, color, position and size of the regions; (d)utilizing the features to compute the similarity of the regionscomprising the foreground and background of successive images in thegroup, thereby leading to a measure of image distance between successiveimages; and (e) delimiting event clusters from the image distances,whereby the event clusters include groups of images pertaining to thesame events.
 8. The method as claimed in claim 7 wherein theblock-by-block comparison in step (b) comprises extracting one or moreof said features from the blocks, utilizing the features to compute thesimilarity of each block with respect to its neighboring blocks, formingregions from similar blocks and merging similar regions into abackground and a foreground.
 9. A computer storage medium havinginstructions stored therein for causing a computer for perform themethod of claim
 7. 10. The method as claimed in claim 7 wherein if apredetermined number of regions formed in step (b) are each less than apredetermined size, then a fixed regions is generated for theforeground.
 11. An event clustering method using foreground andbackground segmentation for clustering images from a group into similarevents, said method including the steps of: (a) dividing each image intoa plurality of blocks, thereby providing block-based images; (b)utilizing a block-by-block comparison to segment each block-based imageinto a plurality of regions, wherein a first combination of regionscomprises a foreground and a second combination of regions comprises abackground; (c) extracting one or more features from the regionscomprising the foreground and background, said features including atleast one of luminosity, color, position and size of the regions; (d)utilizing the features to compute the similarity between each region ofthe combination comprising the foreground of one image in the group andeach region comprising the foreground of another image in the group, andfurther computing the similarity between each region of the combinationcomprising the background of said one image in the group and each regioncomprising the background of said another image in the group; (e)computing a mean value measure of the total similarity betweensuccessive images based on the similarity of all regions included in thecombinations comprising the foreground and background, thereby providinga measure of image distance between said images; and (f) delimitingevent clusters from the image distances, whereby the event clustersinclude groups of images pertaining to the same events.
 12. The methodas claimed in claim 11 wherein the computation of the similarity betweeneach region in step (d) includes a component to account for the relativesizes of the regions
 13. A computer storage medium having instructionsstored therein for causing a computer for perform the method of claim11.
 14. A method for clustering a sequence of images into events basedon similarities between the images, said method comprising the steps of:(a) segmenting each image into regions, including combinations of one ormore regions comprising a foreground and a background; (b) extractinglow-level features from the regions; (c) utilizing the low-levelfeatures to compare the regions comprising the foreground and backgroundof successive images, said comparison generating an image similaritymeasure for the regions comprising the foreground and background of thesuccessive images; (d) combining the image similarity measures for theregions comprising the foreground and background of the successiveimages to obtain a global similarity measure; and (e) delimiting eventclusters by using the global similarity measure.
 15. The method asclaimed in claim 14 wherein said low-level features include at least oneof luminosity, color, position and size of the regions
 16. A method forsegmenting an image into a foreground and a background comprising thesteps of: (a) dividing each image into a plurality of blocks; (b)extracting one or more features from the blocks, said features includingat least one of luminosity, color, position and size of the regions; (c)utilizing the features to generate a similarity measure between eachblock and one or more of its neighboring blocks; (d) identifyingboundary separations between groups of blocks having the leastsimilarity; (e) connecting the boundary separations to form regions; and(f) merging similar regions to form a foreground and a background.
 17. Asystem using foreground and background segmentation for clusteringimages from a group into similar events, said system comprising: (a) afirst module for dividing each image into a plurality of blocks, therebyproviding block-based images, said first module then utilizing ablock-by-block comparison to segment each block-based image into aplurality of regions comprising at least a foreground and a background;(b) a second module for extracting one or more features from the regionscomprising the foreground and background, said features including atleast one of luminosity, color, position and size of the regions; (c) athird module for utilizing the features to compute the similarity of theregions comprising the foreground and background of successive images inthe group, whereby said similarity includes a component to account forthe relative sizes of the regions, said third module computing a meanvalue measure of the total similarity between successive images, therebyproviding a measure of image distance between successive images; and (d)a fourth module for delimiting event clusters from the image distances,whereby the event clusters include groups of images pertaining to thesame events.
 18. The system as claimed in claim 17 wherein the group ofimages are arranged in a chronological order and said third modulefurther utilizes the features to estimate and compare the similarity ofregions comprising foreground and background in every other image in thegroup and computes a measure of the total similarity between every otherimage, thereby providing image distance between successive images andevery other image.
 19. The system as claimed in claim 17 wherein thegroup of images are arranged in a chronological order and the thirdmodule further utilizes the features to estimate and compare thesimilarity of regions comprising foreground and background in everyother two images in the group and computes a measure of the totalsimilarity between every other two images, thereby providing imagedistance between successive images and every other two images.